Memory Management and Garbage Collection in High-Throughput Python AI APIs

Python's automatic garbage collection is great until you're dealing with massive NumPy arrays, PyTorch tensor allocations, and OpenCV image buffers in a multi-threaded web server. Uncollected circular references can cause memory bloat that eventually triggers OOM kills on your VPS.

Explicitly deleting references and calling gc.collect() after heavy batch inference runs has saved our backend services from crashing multiple times. How do you monitor and profile memory leaks in production Python services?